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PLoS One. 2013;8(2):e55512. doi: 10.1371/journal.pone.0055512. Epub 2013 Feb 13.

Prediction of S-glutathionylation sites based on protein sequences.

Author information

1
Institute of Systems Biology, Shanghai University, Shanghai, China.

Abstract

S-glutathionylation, the reversible formation of mixed disulfides between glutathione(GSH) and cysteine residues in proteins, is a specific form of post-translational modification that plays important roles in various biological processes, including signal transduction, redox homeostasis, and metabolism inside cells. Experimentally identifying S-glutathionylation sites is labor-intensive and time consuming, whereas bioinformatics methods provide an alternative way to this problem by predicting S-glutathionylation sites in silico. The bioinformatics approaches give not only candidate sites for further experimental verification but also bio-chemical insights into the mechanism of S-glutathionylation. In this paper, we firstly collect experimentally determined S-glutathionylated proteins and their corresponding modification sites from the literature, and then propose a new method for predicting S-glutathionylation sites by employing machine learning methods based on protein sequence data. Promising results are obtained by our method with an AUC (area under ROC curve) score of 0.879 in 5-fold cross-validation, which demonstrates the predictive power of our proposed method. The datasets used in this work are available at http://csb.shu.edu.cn/SGDB.

PMID:
23418443
PMCID:
PMC3572087
DOI:
10.1371/journal.pone.0055512
[Indexed for MEDLINE]
Free PMC Article

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